code: def forward(
      self,
      *,
      encoder_mb_data: Dict[str, Any],
      decoder_mb_data: Dict[str, Any],
  ):
      gnn_output: GnnOutput = self._gnn(**encoder_mb_data)
  
      loss = self._decoder(
          input_memories=gnn_output.output_node_representations[
              gnn_output.node_idx_references["backbone_nodes"]
          ],
          input_memories_origin_idx=gnn_output.node_graph_idx_reference[
              "backbone_nodes"
          ],
          initial_states=self._get_initial_decoder_states(gnn_output),
          **decoder_mb_data,
      )
      with torch.no_grad():
          self.__loss_sum += float(loss.cpu())
          self.__num_mbs += 1
      return loss
  
imports: from typing_extensions import Final, TypedDict
  import torch
  from typing import Any, Dict, List, NamedTuple, Optional, Tuple, Union
  from ptgnn.baseneuralmodel import AbstractNeuralModel, ModuleWithMetrics
  from ptgnn.baseneuralmodel.utils.data import enforce_not_None
  from ptgnn.neuralmodels.gnn.graphneuralnetwork import GraphNeuralNetwork, GraphNeuralNetworkModel
  from ptgnn.neuralmodels.gnn.structs import GnnOutput, GraphData, TensorizedGraphData
  from ptgnn.neuralmodels.reduceops import AbstractVarSizedElementReduce, ElementsToSummaryRepresentationInput, MultiheadSelfAttentionVarSizedElementReduce, SimpleVarSizedElementReduce
  from ptgnn.neuralmodels.sequence.grucopydecoder import DecoderData, GruCopyingDecoder, GruCopyingDecoderModel, TokenizedOutput
label_desc: Use image node_id 5 for calling the Graph2SeqModule obj's underlying member method code with example usage: obj.forward() and returns: loss